Data science for retail
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Running a company is not only about performing daily tasks as usual the goal is to become more efficient every day. Optimizing business processes is crucial if you want your company to become competitive. In order to do so, businesses need to learn from their own mistakes and create better business strategies. How do they do that? The answer lies in data analytics.

All organizations collect data from many sources and in various forms. They can choose from different tools and techniques to store, process and analyse it. Data analytics is a dynamically growing field. Business insights predictions of the future are very precious. Using the proper type of data analytics can help you outrun your competition easily. You can also use our knowledge to your advantage – visit our Data Science Services and find out how we can help you

Why are there so many types of data analytics?

Each industry requires different approaches and analytics tools. It is important to choose wisely based on the size of your company because advanced analytics solutions can be expensive. Before you choose your analytics systems, you need to think about the type of data you store. Do you have many sources of information? Are you collecting data in multiple different formats? All companies can benefit from data-driven insights for business, but you should think carefully about what your resources are and how complex of an analysis you would like your system to perform. 

In order to turn unstructured data into useful insight, you should leverage big data analytics – it is a must if you deal with huge amounts of data, which are difficult to process using traditional methods. There are four types of data analytics: 

  • descriptive,
  • diagnostic,
  • predictive,
  • prescriptive.

Each kind of big data analytics uses different techniques and will answer different questions. Using them allows you to understand your business and industry better and make the right decisions.

Descriptive analytics – what is happening in my company?

If you want to learn what has happened in your company in recent periods of activity, you can use descriptive analytics. It can also be a first step of a more advanced analysis, because it can help you spot inaccuracies in the data. You won’t get any information about why something happened or what it will cause in the future. You’re just getting an image of the past.

The best example of a tool like this is the popular Google Analytics, which allows users to track the traffic on their websites. What you get is an overview of what happened on the web page in the chosen period how many people visited it, what kind of people were there (demographic data), how long the sessions were or what kind of devices were used in order to view the website. There are many tools like this, presenting gathered information on a clear dashboard, so you can analyse it.

What kind of techniques are used in order to perform descriptive analytics? 

  • Data aggregation the process of collecting data and presenting it in a summarized form.
  • Data mining looking for the patterns and discovering knowlegde hidden beneath the data.
  • Reporting the outcomes of analysis presented in visual or textual form.

How can companies benefit from these kinds of analytics?

You can get simple information about many things and processes in your company. For example the marketing department can learn what kind of and how many users visit the website or profiles on social media. Later, after running analysis, the marketing manager can decide how to modify the marketing strategy in order to attain the desired results. Manufacturers can check the monthly income on each product in order to decide on which product type they should focus on. You can also use this type of analytics to describe customers’ preferences. 

Diagnostic analytics – why particular things are happening in my business? 

In need of deeper analytics? When using diagnostic analytics tools you can learn why something has happened. Let’s assume that there is little interest in content you’ve published on your website or your company is experiencing a significant drop in sales in some period. Diagnostic analytics helps you learn why. 

This kind of analytics usually requires additional data sources to gain the desired business insights. For example, you can see that a huge number of customers chose some popular product and added it to their carts, but in the end they didn’t buy it. You can learn that some customers abandoned their cart or deleted the item from it the first can tell you that clients could have had some problems when finalizing the transaction (due to e-commerce platform loading issues or a non-intuitive user interface in the application) and the second that they were not convinced to buy the product after all.

Diagnostic analytics can help you learn what issues should be fixed or what practices are yielding positive results. The techniques used in this type of analytics are data mining and drill down – it allows the user to dive deeper into specific layers of analyzed data.

Examples of diagnostic analytics

You can use diagnostic analytics to learn what kind of practices have worked well for your social media popularity in the past and which marketing campaigns were not successful and why. Insights from this type of analytics can be really useful for e-commerce in determining if your platform is performing efficiently. 

Predictive analytics forecasting the future of your industry

Predictive analytics is a rather advanced solution it seeks to predict what will happen in your company or the industry. How does it work? Analysts design predictive models and use past trends to forecast possible future events. Business insights gained in this way are very useful in the process of creating business strategies. Your analysts can learn when sales are likely to drop or when the demand for some products will grow. Predictive modeling can be also used by companies who (for example, Uber) use dynamic pricing. 

What kinds of methods are used in this type of analytics?

  • Machine learning ML models recognize patterns hidden beneath the data and make predictions. 
  • Quantitative analysis this method uses mathematical and statistical modeling in order to understand behaviours.
  • Regression analysis a method which allows you to identify the factors that influence some processes in your business

What can you learn by using predictive analytics? 

Many industries can use predictive analytics. In healthcare, it can help predict the risk of some specific group of patients getting sick so the clinic can come up with a strategy to prevent this or minimize the risk. It is widely used in the financial sector for determining if insurance buyers are at some risk thanks to it insurance companies can formulate proper terms for each buyer. We all benefit from predictive analytics every day weather forecasting has improved due to predictive models. Predictive analytics can be used to raise the effectiveness of marketing campaigns, sending them to the exact customers who are most likely to buy the given products. As a matter of fact, this kind of analytics can be useful in every industry.

Prescriptive analytics – find a way to make it real

Whereas the 3 previous types of analytics can give you information about what, why and if something happened/will happen, prescriptive analytics can help you establish what you should do to receive your desired results. So, the answers you get are:

  • How can you take advantage of future events?
  • How should you act to increase profits?
  • What do you do to avoid future problems that your industry may suffer from?

This is the most complex type of analytics you can perform to get business insights for your company. You actually get instructions on how to operate to make your company successful. 

What kind of techniques are used in this analytics? 

  • Artificial intelligence AI uses sets of data to learn patterns and make predictions for the future of your business and industry. 
  • Statistical methods mathematical formulas, models and techniques are used to extract important information from data. 
  • Recommendation engines these systems suggest the best solutions using specific algorithms and data. 
  • Simulation analysis in this process, multiple calculations are performed to learn the possible outcomes of chosen actions.
  • Operations research –  it is about breaking down problems into basic components and then solving it in a few steps using mathematical analysis. 

How can you benefit from prescriptive analytics? It can be used in any company to plan long-term. It is used by entrepreneurs. Perhaps you could take advantage of the highest quality business insights?

What type of business analytics does your business need and how do you implement it?

In order to choose the best data analytics approach and tools, you need to analyse your company’s needs carefully. Think of what kind of data analytics you already use in your company and how important it is for you to gain really deep business insights. If you’re not sure what kind of business analytics solution would serve you best – contact us. Our experienced consultants will be happy to learn about your company’s needs and advise you on the best technologies for you.

Check out our blog for more in-depth articles on Data Science & Advanced Analytics:


  • Laura Kszczanowicz
  • Mikolaj Klepacz

    Mikołaj is a Data Engineer experienced in many Big Data related tools such as Apache Beam, Airflow, Hadoop and Spark. He is no stranger to cloud technologies as he is a 2x Certified Google Cloud Professional and has gained experience working in multiple GCP projects. Always working on improving his clean code writing skills in python. In his free time, he likes travelling and watching NBA basketball.

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